Plot and fit distributions of variables

Histogram of Velocity of all

Log distribution, 0’s make up 3% of all data

sd(log10(CC.TotalData$v[CC.TotalData$v>0]))
[1] 0.543003

Now we’re going to plot each unique krill and their bimodality with a dip test (less than 0.05 is multimodality)

ind <- unique(CC.TotalData$D_V_T)
print(ind)
  [1] 20191118_1__1 20191119_1__4 20191119_1__5 20191119_1__1 20191119_1__2 20191119_1__3 20191119_2__1 20191119_2__2 20191119_2__4 20191119_2__3
 [11] 20191119_2__5 20191119_3__1 20191119_3__2 20191119_3__5 20191119_3__4 20191119_3__3 20191119_5__1 20191119_5__3 20191119_5__4 20191119_5__2
 [21] 20191119_6__1 20191119_6__3 20191119_7__1 20191119_7__2 20191119_7__3 20191119_7__5 20191119_7__4 20191120_2__1 20191120_2__2 20191120_2__3
 [31] 20191120_2__4 20191120_2__5 20191120_4__2 20191120_4__4 20191120_4__3 20191120_4__1 20191121_1__2 20191121_1__3 20191121_1__4 20191121_1__1
 [41] 20191121_2__3 20191121_2__4 20191121_2__5 20191121_2__6 20191121_2__1 20191121_2__2 20191121_4__1 20191121_4__2 20191121_4__3 20191121_4__4
 [51] 20191121_5__3 20191121_5__4 20191121_5__2 20191121_5__1 20191121_6__1 20191121_6__3 20191121_6__2 20191121_6__4 20191121_7__1 20191121_7__2
 [61] 20191121_7__3 20191121_7__4 20191122_2__2 20191122_2__1 20191122_2__3 20191122_2__4 20191122_3__2 20191122_3__3 20191122_3__4 20191122_3__1
 [71] 20191124_10_1 20191124_10_2 20191127_1__3 20191127_1__4 20191127_1__6 20191127_1__7 20191127_1__5 20191127_1__1 20191127_1__2 20191127_4__2
 [81] 20191127_4__1 20191127_4__3 20191127_4__4 20191127_4__5 20191127_4__6 20191129_2__1 20191129_2__2 20191129_2__3 20191129_2__4 20191129_3__1
 [91] 20191129_3__2 20191129_3__3 20191129_3__4 20191129_6__1 20191129_6__2 20191129_6__3 20191129_6__4 20191130_1__1 20191130_1__2 20191130_1__3
[101] 20191130_1__4 20191130_3__1 20191130_3__2 20191130_3__3 20191130_3__4 20191130_6__1 20191130_6__2 20191130_6__3 20191130_6__4 20191130_6__5
[111] 20191201_2__1 20191201_2__2 20191201_2__3 20191201_2__4 20191202_1__1 20191202_1__2 20191202_1__3 20191202_1__4 20191204_2__1 20191204_2__2
[121] 20191204_2__3 20191204_2__4 20191205_10_1 20191205_10_2 20191205_10_3 20191205_10_4 20191212_3__1 20191212_3__2 20191212_3__3
153 Levels: 20191118_1__1 20191119_1__1 20191119_1__2 20191119_1__3 20191119_1__4 20191119_1__5 20191119_2__1 20191119_2__2 20191119_2__3 ... 20191212_3__4
length(ind)
[1] 129
library(diptest)
library(DescTools)

tab <- matrix(data = NA, nrow = 129, ncol =5, byrow = T)
colnames(tab) <- c('dip.test', 'skew', 'mean.velocity', 'sd.velocity', 'Ind')

tab <- as.data.frame(tab)
tab$Ind <- ind
mean.vel <- NULL
sd.vel <- NULL
d.v <- NULL
s.v <- NULL

for (i in 1:length(ind)){
mean.v <- mean(CC.TotalData$v[CC.TotalData$D_V_T==ind[i]])
sd.v <- sd(CC.TotalData$v[CC.TotalData$D_V_T==ind[i]])

mean.vel <- rbind(mean.vel, mean.v)
sd.vel <- rbind(sd.vel, sd.v)


vels <- (CC.TotalData$v[CC.TotalData$D_V_T==ind[i]])
vels <- log10(vels[vels>0])
d <- dip.test(vels)
d.p <- d$p.value
d.v <- rbind(d.v, d.p)

s <- Skew(vels)
s.v <- rbind(s.v, s)
##}

##ind <- unique(CC.TotalData$D_V_T)

##for (i in 1:length(ind)){
 hist(log10(CC.TotalData$v[CC.TotalData$D_V_T==ind[i]]),
     breaks = 50,
     xlab = "Velocity (Log^10 mm/s)",
     main = ind[i],
     sub = d.p)  ## change to d.p or s to print the dip test or skew value as the title instead
}


tab$skew <- s.v
tab$dip.test <- d.v
tab$mean.velocity <- (log10(mean.vel))
tab$sd.velocity <- (log10(sd.vel))
tab
write.table(tab, file = "~/Post-doc/Data/dip.test.skew.vels.csv", sep = ",", col.names = TRUE)

plot(tab)



head(TotalData)
str(TotalData)
'data.frame':   1722390 obs. of  23 variables:
 $ Date       : chr  "20191118" "20191118" "20191118" "20191118" ...
 $ File.name  : chr  "20191118_view1_" "20191118_view1_" "20191118_view1_" "20191118_view1_" ...
 $ X          : num  0.319 NaN 0.318 NaN 0.343 ...
 $ Y          : num  NaN NaN NaN NaN 0.0999 ...
 $ Z          : num  NaN -0.0573 NaN -0.0567 -0.055 ...
 $ Track      : int  1 1 1 1 1 1 1 1 1 1 ...
 $ View       : chr  "1_" "1_" "1_" "1_" ...
 $ D_V_T      : Factor w/ 153 levels "20191118_1__1",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ D_V        : Factor w/ 35 levels "20191118_1_",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Flow.rate  : num  0 0 0 0 0 0 0 0 0 0 ...
 $ Chlorophyll: num  0 0 0 0 0 0 0 0 0 0 ...
 $ Guano      : Factor w/ 2 levels "Absent","Present": 1 1 1 1 1 1 1 1 1 1 ...
 $ Light      : Factor w/ 2 levels "Absent","Present": 2 2 2 2 2 2 2 2 2 2 ...
 $ dx         : num  NaN NaN NaN NaN -0.00115 ...
 $ dy         : num  NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...
 $ dz         : num  NaN NaN NaN 0.00168 NaN ...
 $ d          : num  NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...
 $ vx         : num  NaN NaN NaN NaN -0.0345 ...
 $ vy         : num  NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...
 $ vz         : num  NaN NaN NaN 0.0503 NaN ...
 $ v          : num  NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...
 $ heading    : num  NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...
 $ pitch      : num  NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...
TotalData$Flow.rate <- as.factor(TotalData$Flow.rate)
TotalData$Chlorophyll <- as.factor(TotalData$Chlorophyll)

library(ggplot2)
package 㤼㸱ggplot2㤼㸲 was built under R version 3.6.3
ggplot(TotalData,aes(x=Flow.rate, y=turn.angle, fill=Chlorophyll))+
  geom_boxplot(notch=F, notchwidth=0.3,outlier.shape=1,outlier.size=2, coef=1.5)+
  theme(axis.text=element_text(color="black"))+
  theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.4))+
  theme(panel.grid.minor=element_blank())+
  labs(size= "",x = "Flow Rate (cm/s)", y = "Turn angle (degrees)", title = "               Light")+
  scale_fill_manual(values=c("greenyellow", "green","green1", "green2", "green3", "green4"),name = "Chlorophyll (mg/L)",
                    labels=c("0", "4.6", "6.1", "7.6", "13.5", "19"))+
  facet_grid(~Light, scales = "free_x", space = "free")
Error in FUN(X[[i]], ...) : object 'turn.angle' not found

Now looking at turning angles

Starting to look at bimodality in the variables by merging the TotalData frame with the “tab” table

print (prob)
     
               0        4.3        4.6        6.1        7.6       13.5         19
  0   0.37209302 0.00000000 0.00000000 0.00000000 0.09302326 0.00000000 0.10077519
  0.6 0.01550388 0.03100775 0.03100775 0.03100775 0.00000000 0.02325581 0.00000000
  3   0.03100775 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
  5.9 0.17829457 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
  8.9 0.09302326 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
save.image("~/Post-doc/Data/Total Merged Data File.RData")
---
title: "Checking Distribution for Variables"
output: html_notebook
---

Plot and fit distributions of variables

```{r}

load("C:\\Users\\nicoleh3\\Documents\\Post-doc\\Data\\Total Data Merged File (Nov 2 2021).Rda")

hist(log10(TotalData$v),
     xlab = "Velocity (Log^10 mm/s)")

```

Histogram of Velocity of all

Log distribution, 0's make up 3% of all data

```{r}
CC.TotalData <- na.omit(TotalData)
head(CC.TotalData)
10^mean(log10(CC.TotalData$v[CC.TotalData$v>0]))
sd(log10(CC.TotalData$v[CC.TotalData$v>0]))

```
Now we're going to plot each unique krill and their bimodality with a dip test (less than 0.05 is multimodality)

```{r}
ind <- unique(CC.TotalData$D_V_T)
print(ind)
length(ind)
library(diptest)
library(DescTools)

tab <- matrix(data = NA, nrow = 129, ncol =5, byrow = T)
colnames(tab) <- c('dip.test', 'skew', 'mean.velocity', 'sd.velocity', 'Ind')

tab <- as.data.frame(tab)
tab$Ind <- ind
mean.vel <- NULL
sd.vel <- NULL
d.v <- NULL
s.v <- NULL

for (i in 1:length(ind)){
mean.v <- mean(CC.TotalData$v[CC.TotalData$D_V_T==ind[i]])
sd.v <- sd(CC.TotalData$v[CC.TotalData$D_V_T==ind[i]])

mean.vel <- rbind(mean.vel, mean.v)
sd.vel <- rbind(sd.vel, sd.v)


vels <- (CC.TotalData$v[CC.TotalData$D_V_T==ind[i]])
vels <- log10(vels[vels>0])
d <- dip.test(vels)
d.p <- d$p.value
d.v <- rbind(d.v, d.p)

s <- Skew(vels)
s.v <- rbind(s.v, s)
##}

##ind <- unique(CC.TotalData$D_V_T)

##for (i in 1:length(ind)){
 hist(log10(CC.TotalData$v[CC.TotalData$D_V_T==ind[i]]),
     breaks = 50,
     xlab = "Velocity (Log^10 mm/s)",
     main = ind[i],
     sub = d.p)  ## change to d.p or s to print the dip test or skew value as the title instead
}

tab$skew <- s.v
tab$dip.test <- d.v
tab$mean.velocity <- (log10(mean.vel))
tab$sd.velocity <- (log10(sd.vel))
tab
write.table(tab, file = "~/Post-doc/Data/dip.test.skew.vels.csv", sep = ",", col.names = TRUE)

plot(tab)


head(TotalData)
str(TotalData)
TotalData$Flow.rate <- as.factor(TotalData$Flow.rate)
TotalData$Chlorophyll <- as.factor(TotalData$Chlorophyll)

```


Now looking at turning angles

```{r}
TotalData$turn.anglexy <- atan2(TotalData$X, TotalData$Y)
TotalData$turn.angleyz <- atan2(TotalData$Y, TotalData$Z)


lth <- dim(TotalData)[1]
dx1 <- TotalData$dx[1:(lth-1)]
dx2 <- TotalData$dx[2:lth]
dy1 <- TotalData$dy[1:(lth-1)]
dy2 <- TotalData$dy[2:lth]
dz1 <- TotalData$dz[1:(lth-1)]
dz2 <- TotalData$dz[2:lth]
D <- (dx1*dx2)+(dy1*dy2)+(dz1*dz2)
d1 <- sqrt(dx1^2 + dy1^2 +dz1^2)
d2 <- sqrt(dx2^2 + dy2^2 +dz2^2)

dd <- D/d1/d2
hist(acos(dd)/pi*180)

TotalData$turn.angle <- c(NA, acos(D/d1/d2))/pi*180
head(TotalData)
CC.TotalData <- na.omit(TotalData)
head(CC.TotalData)
tail(CC.TotalData)
str(CC.TotalData)
CC.TotalData$Flow.rate <- as.character(CC.TotalData$Flow.rate)
CC.TotalData$Chlorophyll<- as.character(CC.TotalData$Chlorophyll)
CC.TotalData$Guano <- as.character(CC.TotalData$Guano)
CC.TotalData$Light <- as.character(CC.TotalData$Light)


CC.TotalData$Flow.rate <- as.numeric(CC.TotalData$Flow.rate)
CC.TotalData$Chlorophyll<- as.numeric(CC.TotalData$Chlorophyll)

head(CC.TotalData)

ind <- unique(CC.TotalData$D_V_T)

for (i in 1:length(ind)){
hist(CC.TotalData$turn.angle[CC.TotalData$D_V_T==ind[i]],
       breaks = 50,
     xlab = "Turn Angles",
     main = "") 
}

plot(CC.TotalData$Flow.rate, CC.TotalData$turn.angle, main = "", xlab = "Flow Rate (cm/s)", ylab = "Turn angle (degrees)")
plot(CC.TotalData$Chlorophyll, CC.TotalData$turn.angle, main = "", xlab = "Chlorophyll (mg/L)", ylab = "Turn angle (degrees)")

library(ggplot2)
ggplot(TotalData,aes(x=Flow.rate, y=log10(v), fill=Chlorophyll))+
  geom_boxplot(notch=F, notchwidth=0.3,outlier.shape=1,outlier.size=2, coef=1.5)+
  theme(axis.text=element_text(color="black"))+
  theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.4))+
  theme(panel.grid.minor=element_blank())+
  labs(size= "",x = "Flow Rate (cm/s)", y = "Velocity (Log transformed)(cm/s)", title = "Light") +
  scale_fill_manual(values=c("greenyellow", "yellowgreen","lightgreen", "green", "green3", "green4", "darkgreen"),name = "Chlorophyll (mg/L)",
                    labels=c("0", "4.3", "4.6", "6.1", "7.6", "13.5", "19"))+
  facet_grid(~Light, scales = "free_x", space = "free")

ggplot(TotalData,aes(x=Flow.rate, y=turn.angle, fill=Chlorophyll))+
  geom_boxplot(notch=F, notchwidth=0.3,outlier.shape=1,outlier.size=2, coef=1.5)+
  theme(axis.text=element_text(color="black"))+
  theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.4))+
  theme(panel.grid.minor=element_blank())+
  labs(size= "",x = "Flow Rate (cm/s)", y = "Turn Angle (degrees)", title = "Light") +
  scale_fill_manual(values=c("greenyellow", "yellowgreen","lightgreen", "green", "green3", "green4", "darkgreen"),name = "Chlorophyll (mg/L)",
                    labels=c("0", "4.3", "4.6", "6.1", "7.6", "13.5", "19"))+
  facet_grid(~Light, scales = "free_x", space = "free")

```






Starting to look at bimodality in the variables by merging the TotalData frame with the "tab" table
```{r}
##aggregating Complete cases of TotalData so we can merge it with tab data

AGG_TD <- aggregate(CC.TotalData, by = list(CC.TotalData$D_V_T), FUN = mean)
head(AGG_TD)
AGG_TD <- AGG_TD[ -c(2:3, 8:10) ]
colnames(AGG_TD) <- c("Ind", "X", "Y", "Z", "Track", "Flow.Rate", "Chlorophyll", "Guano", "Light", "dx", "dy", "dz", "d", "vx", "vy", "vz", "v", "heading", "pitch", "turn.anglexy", "turn.angleyz", "turn.angle")
head(AGG_TD)
tail(AGG_TD)


tab_AGG <- merge(AGG_TD, tab, by = "Ind")
head(tab_AGG)
str(tab_AGG)
tab_AGG$Flow.Rate <- as.factor(tab_AGG$Flow.Rate)
tab_AGG$Chlorophyll <- as.factor(tab_AGG$Chlorophyll)

freq <- table(tab_AGG$Flow.Rate, tab_AGG$Chlorophyll)
print(freq)
prob <- prop.table(freq) ##Relative Frequency Table
print (prob)

 ##starting to plot the dip test and skew in the variables
plot(tab_AGG$Flow.Rate, tab_AGG$dip.test, xlab = "Flow Rate (cm/s)", ylab = "Dip Test (p.value)")
plot(tab_AGG$Flow.Rate, tab_AGG$skew, xlab = "Flow Rate (cm/s)", ylab = "Skew Test (p.value)")
plot(tab_AGG$Chlorophyll, tab_AGG$dip.test, xlab = "Chlorophyll (mg/L)", ylab = "Dip Test (p.value)")
plot(tab_AGG$Chlorophyll, tab_AGG$skew, xlab = "Chlorophyll (mg/L)", ylab = "Skew Test (p.value)")


for (i in 1:length(ind)){
plot(CC.TotalData$turn.angle[CC.TotalData$D_V_T==ind[i]], log10(CC.TotalData$v[CC.TotalData$D_V_T==ind[i]]),
            xlab = "Turn Angles",
     ylab = "Velocity",
          main = "") 
}

```


```{r}
save.image("~/Post-doc/Data/Total Merged Data File.RData")

```



